Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

Agrawal, Aditi; Paulus, Rajeev

doi:10.11591/ijece.v11i4.pp3176-3185

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…The ASUS model also gave the best PUE with a value of 0.97 (closest to 1.0 target), while the other models gave the same PUE value of 1.54. The performance of the developed ASUS model is in line with the results of adapted power consumption strategy proposed by [23,27,28].…”

Section: Energy Consumption (Ec) and Power Usage Effectiveness (Pue)supporting

confidence: 72%

Prediction of Power Consumption Utilization in a Cloud Computing Data Centre using Kalman Filter parameters with Genetic Algorithm

Afolabi

Adebisi

Adoghe

2023

IJEEI

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Data Centre (DC) has become a critical computing infrastructure that is essential to modern society by providing services such as cloud computing, Internet of Things (IoT) and big data. However, the cost of maintaining DC continues to rise as the demand for information technology services increase and this situation is further exacerbated in a country like Nigeria where there is highly unstable power supply from the national grid. The optimization of energy consumption in cloud computing DC using Genetic Algorithm (GA) to minimize the consumption of energy thereby extending network lifespan was one of the techniques used for optimization of power consumption. But the optimization was carried out with the assumption that all the parts of the modular server that are not carrying traffic is on idle mode and not completely off which consumes extra power compare to when it is completely off. Therefore, this work proposed optimization of power consumption utilization in a cloud computing DC using Kalman Filter (KF) with GA. Historical consumption trend and network traffic is analyzed to reduce the amount spent on power with assumption that servers in the DC operate as modular units which can be powered separately as required, in contrast to keeping entire servers always powered. Data from five different servers were collected from MTN Abuja DC in Nigeria. The servers were named BSC 13, BSC 14, BSC 15, RNC 05 and RNC 06. These consist of data recorded for two year -5th January to 30th December 2019 as well as 5th January to 31st December 2020. The GA optimizer is used to obtain the best possible values for the Kalman Filter (KF) parameters. Then, the KF model is used to predict the future power consumption value on hourly basis for each day of the week. The proposed model gives low power consumption with accurate prediction when compared with the existing models.

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Section: Energy Consumption (Ec) and Power Usage Effectiveness (Pue)supporting

confidence: 72%

Prediction of Power Consumption Utilization in a Cloud Computing Data Centre using Kalman Filter parameters with Genetic Algorithm

Afolabi

Adebisi

Adoghe

2023

IJEEI

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“…Engine. An expert system, which is just a collection of computer programs [21], is incomplete without an inference engine. is system employs a method of reasoning that combines forward and backward reasoning.…”

Section: Inferencementioning

confidence: 99%

Research on Expert System of Japanese Auxiliary Teaching Based on BP Neural Network

Chu

2022

Mobile Information Systems

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College Japanese teaching is the cradle of Japanese professional development. With the rising frequency of interactions with Japan in our country in the fields of politics, economy, trade, and other fields, Japanese as a professional discipline is exhibiting popularization, universalization, and folkization features. However, the ever-emerging trend of Japanese as an application tool for worldwide communication has rendered professional Japanese instruction in colleges insufficient. To meet the needs and growth of society, as well as to address the problems of teacher shortages and a lack of attention to students’ fundamental knowledge in the reform of Japanese language teaching in colleges, the study uses an expert system as the theoretical foundation and combines BP neural network technology to design an auxiliary teaching system with a friendly interface, strong versatility, and extensibility for Japanese language teachers and students. Teachers can use this technique to organize the test by classifying and summarizing the test questions based on the knowledge points and complexity of the questions. Students can utilize this system to learn on their own, and by identifying weak links in their knowledge points, they can practice more effectively and create a multiplier impact with half the work. Finally, the whole system is designed and implemented in accordance with the software development process. It has been demonstrated that the system can provide realistic results and has good application value after a huge quantity of data testing and operation.

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“…Two controllers are used in parallel to capture the lane for green signal and calculate appropriate signal time as per the real time traffic. This paper uses fuzzy models [20] to change traffic signals, which in-turn provides clearance to the emergency vehicle. Traffic light are managed using peripheral interface controller.…”

Section: Introductionmentioning

confidence: 99%

Automated signal pre-emption system for emergency vehicles using internet of things

Sothenahalli Krishnaraju,

Thimmasandra Narayanappa,

Kathavate

et al. 2024

IJ-AI

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<span lang="EN-US">Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.</span>

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Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

Cited by 8 publications

References 12 publications

Prediction of Power Consumption Utilization in a Cloud Computing Data Centre using Kalman Filter parameters with Genetic Algorithm

Prediction of Power Consumption Utilization in a Cloud Computing Data Centre using Kalman Filter parameters with Genetic Algorithm

Research on Expert System of Japanese Auxiliary Teaching Based on BP Neural Network

Automated signal pre-emption system for emergency vehicles using internet of things

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